Reinforcement learning policies can often be learnt in simulation or in a laboratory. In this case, on top of their local observation histories, agents may have access to the full environment state and share each other's policies and experiences during training. The framework of centralised training with decentralised execution (CTDE) [1]}, [2]} formalises the use of centralised information to facilitate the training of decentralisable policies.
